Post-Disaster Damage Assessment by Fusing Satellite and Crowd-Sourced Images

A post-disaster damage assessment system that fuses satellite and crowd-sourced images.

A post-disaster damage assessment system that fuses satellite and crowd-sourced images. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with images or video: building a dataset, training or fine-tuning a vision model, measuring its performance and wrapping it in a usable tool.

The main parts are dataset preparation and annotation, model training or fine-tuning, evaluation with standard metrics, plus visual examples, and an interface that works on uploaded images or live video. The reference build uses Rasterio, Python, OpenCV, PyTorch, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.